{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h2tmtetBZsTE"
      },
      "source": [
        "##### Copyright 2023 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DsTOPNzMZsTT"
      },
      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YRlNzHiLN87u"
      },
      "source": [
        "# Train Perceiver model for down streaming tasks\n",
        "\n",
        "This tutorial demonstrates how to use [Perciever](https://arxiv.org/abs/2107.14795) model for down streaming tasks using Tensorflow Model Garden.\n",
        "\n",
        "[Tensorflow Model Garden](https://www.tensorflow.org/tfmodels) contains a collection of state-of-the-art models, implemented with TensorFlow's high-level APIs. The implementations demonstrate the best practices for modeling, letting users to take full advantage of TensorFlow for their research and product development."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pRFcanY4y5lV"
      },
      "source": [
        "\n",
        "## Clone models repository"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IPIuUUMxy5Ei"
      },
      "outputs": [],
      "source": [
        "!git clone -q https://github.com/tensorflow/models.git"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B7OUNfFxymIm"
      },
      "source": [
        "## Install necessary dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "executionInfo": {
          "elapsed": 97312,
          "status": "ok",
          "timestamp": 1689009983377,
          "user": {
            "displayName": "Siva Sravana Kumar Neeli",
            "userId": "06669604936988620923"
          },
          "user_tz": 420
        },
        "id": "bpSKXUuQpVaW",
        "outputId": "7d0006a4-68bf-4aad-d49f-c93a115b32fe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m524.1/524.1 MB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m86.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.6/5.6 MB\u001b[0m \u001b[31m95.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m440.8/440.8 kB\u001b[0m \u001b[31m44.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m241.2/241.2 kB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m175.1/175.1 kB\u001b[0m \u001b[31m19.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.6/43.6 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m35.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m118.9/118.9 kB\u001b[0m \u001b[31m14.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.6/17.6 MB\u001b[0m \u001b[31m84.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for pyyaml (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[33m  WARNING: The scripts f2py, f2py3 and f2py3.10 are installed in '/root/.local/bin' which is not on PATH.\n",
            "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
            "\u001b[0m\u001b[33m  WARNING: The script sacrebleu is installed in '/root/.local/bin' which is not on PATH.\n",
            "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
            "\u001b[0m\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "numba 0.56.4 requires numpy\u003c1.24,\u003e=1.18, but you have numpy 1.25.1 which is incompatible.\n",
            "tensorflow 2.13.0 requires numpy\u003c=1.24.3,\u003e=1.22, but you have numpy 1.25.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.5/6.5 MB\u001b[0m \u001b[31m48.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.3/17.3 MB\u001b[0m \u001b[31m14.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "numba 0.56.4 requires numpy\u003c1.24,\u003e=1.18, but you have numpy 1.24.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install -q tensorflow==2.13.0\n",
        "!pip install -q -U tensorflow_datasets\n",
        "!pip install -q --user -r models/official/requirements.txt\n",
        "!pip install -q tensorflow-text==2.13.0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SJ50D6KvW6cK"
      },
      "source": [
        "**Note**: Please restart the runtime once libraries are installed"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S2qSVWakysDC"
      },
      "source": [
        "## Please set the Python path with `os.environ` for models directory"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vnaE50DGAg5H"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "os.environ['PYTHONPATH'] += \":/content/models\"\n",
        "\n",
        "import sys\n",
        "sys.path.append(\"/content/models\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5BPkQZBCzMSm"
      },
      "source": [
        "## Import necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "executionInfo": {
          "elapsed": 16820,
          "status": "ok",
          "timestamp": 1689010089176,
          "user": {
            "displayName": "Siva Sravana Kumar Neeli",
            "userId": "06669604936988620923"
          },
          "user_tz": 420
        },
        "id": "moOdkRC01sdZ",
        "outputId": "ec50aab3-57c3-4b9e-dd9f-138338111e98"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.13.0\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import pprint\n",
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "\n",
        "from IPython import display\n",
        "from official.core import task_factory\n",
        "from official.core import train_lib\n",
        "from official.core import train_utils\n",
        "from official.projects.perceiver.tasks import sentence_prediction\n",
        "from official.projects.perceiver.configs import perceiver as exp_cfg\n",
        "from official.nlp.modeling.layers import FastWordpieceBertTokenizer\n",
        "from official.nlp.modeling.layers import BertPackInputs\n",
        "\n",
        "\n",
        "pp = pprint.PrettyPrinter(indent=4) # Set Pretty Print Indentation\n",
        "print(tf.__version__) # Check the version of tensorflow used"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZZ4ZNn3ezW0c"
      },
      "source": [
        "## Download `glue/mrpc` dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 931,
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          ]
        },
        "executionInfo": {
          "elapsed": 5649,
          "status": "ok",
          "timestamp": 1689010130551,
          "user": {
            "displayName": "Siva Sravana Kumar Neeli",
            "userId": "06669604936988620923"
          },
          "user_tz": 420
        },
        "id": "izlrDcj62Hlh",
        "outputId": "c4009825-4f49-41a3-d774-29166d1eb352"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset 1.43 MiB (download: 1.43 MiB, generated: 1.74 MiB, total: 3.17 MiB) to /root/tensorflow_datasets/glue/mrpc/2.0.0...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6c4cee474109471886f7dff896ff6d33",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Dl Completed...: 0 url [00:00, ? url/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9b2125597d1c4f48b951ea64df65aaaf",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1377ec55510c471f957c5be3042ef36c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating splits...:   0%|          | 0/3 [00:00\u003c?, ? splits/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d8f3a76c74374d4ca022aba7b184714a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train examples...:   0%|          | 0/3668 [00:00\u003c?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "58dad5720b0f404fb3b898dba90055ad",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/glue/mrpc/2.0.0.incompleteGFRJBN/glue-train.tfrecord*...:   0%|          |…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "524dc0d66c4047c6a9d3304cc0a87f33",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating validation examples...:   0%|          | 0/408 [00:00\u003c?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "10a08d86c6c049c7b4b2231db6207526",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/glue/mrpc/2.0.0.incompleteGFRJBN/glue-validation.tfrecord*...:   0%|      …"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bc072d37e96a4e1d80817a3b9eefffa5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating test examples...:   0%|          | 0/1725 [00:00\u003c?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e3caa87a46f948038953eb53ee9eb49c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/glue/mrpc/2.0.0.incompleteGFRJBN/glue-test.tfrecord*...:   0%|          | …"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset glue downloaded and prepared to /root/tensorflow_datasets/glue/mrpc/2.0.0. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "tfds.core.DatasetInfo(\n",
              "    name='glue',\n",
              "    full_name='glue/mrpc/2.0.0',\n",
              "    description=\"\"\"\n",
              "    GLUE, the General Language Understanding Evaluation benchmark\n",
              "    (https://gluebenchmark.com/) is a collection of resources for training,\n",
              "    evaluating, and analyzing natural language understanding systems.\n",
              "    \"\"\",\n",
              "    config_description=\"\"\"\n",
              "    The Microsoft Research Paraphrase Corpus (Dolan \u0026 Brockett, 2005) is a corpus of\n",
              "    sentence pairs automatically extracted from online news sources, with human annotations\n",
              "    for whether the sentences in the pair are semantically equivalent.\n",
              "    \"\"\",\n",
              "    homepage='https://www.microsoft.com/en-us/download/details.aspx?id=52398',\n",
              "    data_path=PosixGPath('/tmp/tmpyoq7f3i8tfds'),\n",
              "    file_format=tfrecord,\n",
              "    download_size=1.43 MiB,\n",
              "    dataset_size=1.74 MiB,\n",
              "    features=FeaturesDict({\n",
              "        'idx': int32,\n",
              "        'label': ClassLabel(shape=(), dtype=int64, num_classes=2),\n",
              "        'sentence1': Text(shape=(), dtype=string),\n",
              "        'sentence2': Text(shape=(), dtype=string),\n",
              "    }),\n",
              "    supervised_keys=None,\n",
              "    disable_shuffling=False,\n",
              "    splits={\n",
              "        'test': \u003cSplitInfo num_examples=1725, num_shards=1\u003e,\n",
              "        'train': \u003cSplitInfo num_examples=3668, num_shards=1\u003e,\n",
              "        'validation': \u003cSplitInfo num_examples=408, num_shards=1\u003e,\n",
              "    },\n",
              "    citation=\"\"\"@inproceedings{dolan2005automatically,\n",
              "      title={Automatically constructing a corpus of sentential paraphrases},\n",
              "      author={Dolan, William B and Brockett, Chris},\n",
              "      booktitle={Proceedings of the Third International Workshop on Paraphrasing (IWP2005)},\n",
              "      year={2005}\n",
              "    }\n",
              "    @inproceedings{wang2019glue,\n",
              "      title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n",
              "      author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n",
              "      note={In the Proceedings of ICLR.},\n",
              "      year={2019}\n",
              "    }\n",
              "    \n",
              "    Note that each GLUE dataset has its own citation. Please see the source to see\n",
              "    the correct citation for each contained dataset.\"\"\",\n",
              ")"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tfds_name = 'glue/mrpc'\n",
        "ds,ds_info = tfds.load(tfds_name,\n",
        "                       with_info=True)\n",
        "ds_info"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZXjm5M2Tzey6"
      },
      "source": [
        "## Download bert base checkpoint for vocab file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "executionInfo": {
          "elapsed": 10341,
          "status": "ok",
          "timestamp": 1689010145419,
          "user": {
            "displayName": "Siva Sravana Kumar Neeli",
            "userId": "06669604936988620923"
          },
          "user_tz": 420
        },
        "id": "xR3t8TY-voKQ",
        "outputId": "945fac79-c397-4157-d33b-8adc6a9edb18"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2023-07-10 17:28:55--  https://storage.googleapis.com/tf_model_garden/nlp/bert/v3/uncased_L-12_H-768_A-12.tar.gz\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.196.128, 173.194.215.128, 173.194.216.128, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.196.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 405351325 (387M) [application/octet-stream]\n",
            "Saving to: ‘./uncased_L-12_H-768_A-12.tar.gz’\n",
            "\n",
            "./uncased_L-12_H-76 100%[===================\u003e] 386.57M   120MB/s    in 3.2s    \n",
            "\n",
            "2023-07-10 17:28:58 (120 MB/s) - ‘./uncased_L-12_H-768_A-12.tar.gz’ saved [405351325/405351325]\n",
            "\n",
            "uncased_L-12_H-768_A-12/\n",
            "uncased_L-12_H-768_A-12/vocab.txt\n",
            "uncased_L-12_H-768_A-12/bert_model.ckpt.index\n",
            "uncased_L-12_H-768_A-12/bert_model.ckpt.data-00000-of-00001\n",
            "uncased_L-12_H-768_A-12/params.yaml\n",
            "uncased_L-12_H-768_A-12/bert_config.json\n"
          ]
        }
      ],
      "source": [
        "!wget https://storage.googleapis.com/tf_model_garden/nlp/bert/v3/uncased_L-12_H-768_A-12.tar.gz -O ./uncased_L-12_H-768_A-12.tar.gz\n",
        "!tar -zxvf ./uncased_L-12_H-768_A-12.tar.gz -C ./\n",
        "!rm ./uncased_L-12_H-768_A-12.tar.gz"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ILW4p_gVP4pg"
      },
      "source": [
        "## Configure the perceiver model for custom dataset training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cAlksY6nzdQY"
      },
      "outputs": [],
      "source": [
        "gs_folder_bert = \"./uncased_L-12_H-768_A-12\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AzCEMctzz5N4"
      },
      "source": [
        "### Load the registered configuration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qS4-ZWxFBH7K"
      },
      "outputs": [],
      "source": [
        "exp_config = exp_cfg.exp_factory.get_exp_config('perceiver/word_piece_raw_sentence_prediction')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4c5gFEvwPd7_"
      },
      "source": [
        "### Change the parameters required to train the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m6-OhvAUyfXT"
      },
      "outputs": [],
      "source": [
        "BATCH_SIZE = 8\n",
        "epochs = 5\n",
        "vocab_file = './uncased_L-12_H-768_A-12/vocab.txt'\n",
        "\n",
        "\n",
        "train_data_size = ds_info.splits['train'].num_examples\n",
        "validation_data_size = ds_info.splits['validation'].num_examples\n",
        "steps_per_epoch = int(train_data_size / BATCH_SIZE)\n",
        "num_train_steps = steps_per_epoch * epochs\n",
        "validation_steps = int(validation_data_size / BATCH_SIZE)\n",
        "warmup_steps = int(0.1 * num_train_steps)\n",
        "initial_learning_rate = 2e-5\n",
        "\n",
        "\n",
        "exp_config.runtime.num_gpus = 1\n",
        "exp_config.runtime.enable_xla = False\n",
        "exp_config.runtime.mixed_precision_dtype = 'mixed_bfloat16'\n",
        "exp_config.task.model.num_classes = 2\n",
        "\n",
        "exp_config.task.train_data.tfds_name = 'glue/mrpc'\n",
        "exp_config.task.train_data.tfds_split = 'train'\n",
        "exp_config.task.train_data.text_fields = ['sentence1', 'sentence2']\n",
        "exp_config.task.train_data.global_batch_size = BATCH_SIZE\n",
        "exp_config.task.train_data.lower_case = True\n",
        "exp_config.task.train_data.tokenization = 'WordPiece'\n",
        "exp_config.task.train_data.vocab_file = vocab_file\n",
        "\n",
        "exp_config.task.validation_data.tfds_name = 'glue/mrpc'\n",
        "exp_config.task.validation_data.tfds_split = 'validation'\n",
        "exp_config.task.validation_data.text_fields = ['sentence1', 'sentence2']\n",
        "exp_config.task.validation_data.global_batch_size = BATCH_SIZE\n",
        "exp_config.task.validation_data.lower_case = True\n",
        "exp_config.task.validation_data.tokenization = 'WordPiece'\n",
        "exp_config.task.validation_data.vocab_file = vocab_file\n",
        "\n",
        "exp_config.trainer.checkpoint_interval = steps_per_epoch\n",
        "exp_config.trainer.optimizer_config.learning_rate.polynomial.initial_learning_rate = initial_learning_rate\n",
        "exp_config.trainer.optimizer_config.learning_rate.polynomial.decay_steps = num_train_steps\n",
        "exp_config.trainer.optimizer_config.warmup.polynomial.warmup_steps = warmup_steps\n",
        "exp_config.trainer.steps_per_loop = steps_per_epoch\n",
        "exp_config.trainer.summary_interval = steps_per_epoch\n",
        "exp_config.trainer.train_steps = num_train_steps\n",
        "exp_config.trainer.validation_interval = steps_per_epoch\n",
        "exp_config.trainer.validation_steps =  validation_steps"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LYP-_WCHnDHd"
      },
      "source": [
        "### Detect the hardware"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "executionInfo": {
          "elapsed": 166,
          "status": "ok",
          "timestamp": 1689010193979,
          "user": {
            "displayName": "Siva Sravana Kumar Neeli",
            "userId": "06669604936988620923"
          },
          "user_tz": 420
        },
        "id": "tjp6Ekql21_U",
        "outputId": "72c0c030-0c1a-4edc-e023-82c98b4b0de2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Running on single GPU  /device:GPU:0\n",
            "Number of accelerators:  1\n"
          ]
        }
      ],
      "source": [
        "try:\n",
        "  tpu_resolver = tf.distribute.cluster_resolver.TPUClusterResolver() # TPU detection\n",
        "except ValueError:\n",
        "  tpu_resolver = None\n",
        "  gpus = tf.config.experimental.list_logical_devices(\"GPU\")\n",
        "\n",
        "# Select appropriate distribution strategy\n",
        "if tpu_resolver:\n",
        "  tf.config.experimental_connect_to_cluster(tpu_resolver)\n",
        "  tf.tpu.experimental.initialize_tpu_system(tpu_resolver)\n",
        "  distribution_strategy = tf.distribute.experimental.TPUStrategy(tpu_resolver)\n",
        "  print('Running on TPU ', tpu_resolver.cluster_spec().as_dict()['worker'])\n",
        "elif len(gpus) \u003e 1:\n",
        "  distribution_strategy = tf.distribute.MirroredStrategy([gpu.name for gpu in gpus])\n",
        "  print('Running on multiple GPUs ', [gpu.name for gpu in gpus])\n",
        "elif len(gpus) == 1:\n",
        "  distribution_strategy = tf.distribute.get_strategy() # default strategy that works on CPU and single GPU\n",
        "  print('Running on single GPU ', gpus[0].name)\n",
        "else:\n",
        "  distribution_strategy = tf.distribute.get_strategy() # default strategy that works on CPU and single GPU\n",
        "  print('Running on CPU')\n",
        "\n",
        "print(\"Number of accelerators: \", distribution_strategy.num_replicas_in_sync)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GxMDFCQhPjSW"
      },
      "source": [
        "### Print the modified configuration."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "executionInfo": {
          "elapsed": 376,
          "status": "ok",
          "timestamp": 1689010197252,
          "user": {
            "displayName": "Siva Sravana Kumar Neeli",
            "userId": "06669604936988620923"
          },
          "user_tz": 420
        },
        "id": "pep0gabI24_R",
        "outputId": "eaadce22-2a36-4fbe-e089-ba2fdc547d26"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{   'runtime': {   'all_reduce_alg': None,\n",
            "                   'batchnorm_spatial_persistent': False,\n",
            "                   'dataset_num_private_threads': None,\n",
            "                   'default_shard_dim': -1,\n",
            "                   'distribution_strategy': 'mirrored',\n",
            "                   'enable_xla': False,\n",
            "                   'gpu_thread_mode': None,\n",
            "                   'loss_scale': None,\n",
            "                   'mixed_precision_dtype': 'mixed_bfloat16',\n",
            "                   'num_cores_per_replica': 1,\n",
            "                   'num_gpus': 1,\n",
            "                   'num_packs': 1,\n",
            "                   'per_gpu_thread_count': 0,\n",
            "                   'run_eagerly': False,\n",
            "                   'task_index': -1,\n",
            "                   'tpu': None,\n",
            "                   'tpu_enable_xla_dynamic_padder': None,\n",
            "                   'use_tpu_mp_strategy': False,\n",
            "                   'worker_hosts': None},\n",
            "    'task': {   'allow_image_summary': False,\n",
            "                'differential_privacy_config': None,\n",
            "                'hub_module_url': '',\n",
            "                'init_checkpoint': '',\n",
            "                'init_cls_pooler': False,\n",
            "                'metric_type': 'accuracy',\n",
            "                'model': {   'decoder': {   'd_latents': 1280,\n",
            "                                            'd_model': 768,\n",
            "                                            'decoder': {   'num_heads': 8,\n",
            "                                                           'output_last_dim': 768,\n",
            "                                                           'qk_last_dim': 256,\n",
            "                                                           'use_query_residual': False,\n",
            "                                                           'v_last_dim': 768},\n",
            "                                            'output_index_dim': 1,\n",
            "                                            'position_encoding_intializer_stddev': 0.02,\n",
            "                                            'z_index_dim': 256},\n",
            "                             'encoder': {   'd_latents': 1280,\n",
            "                                            'd_model': 768,\n",
            "                                            'embedding_initializer_stddev': 0.02,\n",
            "                                            'embedding_width': 768,\n",
            "                                            'encoder': {   'att_init_scale': 1.0,\n",
            "                                                           'cross_attention_num_heads': 8,\n",
            "                                                           'cross_attention_widening_factor': 1,\n",
            "                                                           'dense_init_scale': 1.0,\n",
            "                                                           'dropout_attn_prob': 0.0,\n",
            "                                                           'dropout_prob': 0.0,\n",
            "                                                           'norm_epsilon': 1e-05,\n",
            "                                                           'num_blocks': 1,\n",
            "                                                           'num_self_attends_per_block': 26,\n",
            "                                                           'qk_last_dim': 256,\n",
            "                                                           'self_attention_num_heads': 8,\n",
            "                                                           'self_attention_widening_factor': 1,\n",
            "                                                           'v_last_dim': 1280},\n",
            "                                            'input_position_encoding_intializer_stddev': 0.02,\n",
            "                                            'max_seq_len': 512,\n",
            "                                            'vocab_size': 30522,\n",
            "                                            'z_index_dim': 256,\n",
            "                                            'z_pos_enc_init_scale': 0.02},\n",
            "                             'num_classes': 2,\n",
            "                             'use_encoder_pooler': False},\n",
            "                'name': None,\n",
            "                'train_data': {   'apply_tf_data_service_before_batching': False,\n",
            "                                  'autotune_algorithm': None,\n",
            "                                  'block_length': 1,\n",
            "                                  'cache': False,\n",
            "                                  'cycle_length': None,\n",
            "                                  'deterministic': None,\n",
            "                                  'drop_remainder': True,\n",
            "                                  'enable_shared_tf_data_service_between_parallel_trainers': False,\n",
            "                                  'enable_tf_data_service': False,\n",
            "                                  'file_type': 'tfrecord',\n",
            "                                  'global_batch_size': 8,\n",
            "                                  'include_example_id': False,\n",
            "                                  'input_path': '',\n",
            "                                  'is_training': True,\n",
            "                                  'label_field': 'label',\n",
            "                                  'label_type': 'int',\n",
            "                                  'lower_case': True,\n",
            "                                  'prefetch_buffer_size': None,\n",
            "                                  'preprocessing_hub_module_url': '',\n",
            "                                  'seed': None,\n",
            "                                  'seq_length': 128,\n",
            "                                  'sharding': True,\n",
            "                                  'shuffle_buffer_size': 100,\n",
            "                                  'text_fields': ['sentence1', 'sentence2'],\n",
            "                                  'tf_data_service_address': None,\n",
            "                                  'tf_data_service_job_name': None,\n",
            "                                  'tfds_as_supervised': False,\n",
            "                                  'tfds_data_dir': '',\n",
            "                                  'tfds_name': 'glue/mrpc',\n",
            "                                  'tfds_skip_decoding_feature': '',\n",
            "                                  'tfds_split': 'train',\n",
            "                                  'tokenization': 'WordPiece',\n",
            "                                  'trainer_id': None,\n",
            "                                  'vocab_file': './uncased_L-12_H-768_A-12/vocab.txt'},\n",
            "                'validation_data': {   'apply_tf_data_service_before_batching': False,\n",
            "                                       'autotune_algorithm': None,\n",
            "                                       'block_length': 1,\n",
            "                                       'cache': False,\n",
            "                                       'cycle_length': None,\n",
            "                                       'deterministic': None,\n",
            "                                       'drop_remainder': True,\n",
            "                                       'enable_shared_tf_data_service_between_parallel_trainers': False,\n",
            "                                       'enable_tf_data_service': False,\n",
            "                                       'file_type': 'tfrecord',\n",
            "                                       'global_batch_size': 8,\n",
            "                                       'include_example_id': False,\n",
            "                                       'input_path': '',\n",
            "                                       'is_training': True,\n",
            "                                       'label_field': 'label',\n",
            "                                       'label_type': 'int',\n",
            "                                       'lower_case': True,\n",
            "                                       'prefetch_buffer_size': None,\n",
            "                                       'preprocessing_hub_module_url': '',\n",
            "                                       'seed': None,\n",
            "                                       'seq_length': 128,\n",
            "                                       'sharding': True,\n",
            "                                       'shuffle_buffer_size': 100,\n",
            "                                       'text_fields': [   'sentence1',\n",
            "                                                          'sentence2'],\n",
            "                                       'tf_data_service_address': None,\n",
            "                                       'tf_data_service_job_name': None,\n",
            "                                       'tfds_as_supervised': False,\n",
            "                                       'tfds_data_dir': '',\n",
            "                                       'tfds_name': 'glue/mrpc',\n",
            "                                       'tfds_skip_decoding_feature': '',\n",
            "                                       'tfds_split': 'validation',\n",
            "                                       'tokenization': 'WordPiece',\n",
            "                                       'trainer_id': None,\n",
            "                                       'vocab_file': './uncased_L-12_H-768_A-12/vocab.txt'}},\n",
            "    'trainer': {   'allow_tpu_summary': False,\n",
            "                   'best_checkpoint_eval_metric': '',\n",
            "                   'best_checkpoint_export_subdir': '',\n",
            "                   'best_checkpoint_metric_comp': 'higher',\n",
            "                   'checkpoint_interval': 458,\n",
            "                   'continuous_eval_timeout': 3600,\n",
            "                   'eval_tf_function': True,\n",
            "                   'eval_tf_while_loop': False,\n",
            "                   'loss_upper_bound': 1000000.0,\n",
            "                   'max_to_keep': 5,\n",
            "                   'optimizer_config': {   'ema': None,\n",
            "                                           'learning_rate': {   'polynomial': {   'cycle': False,\n",
            "                                                                                  'decay_steps': 2290,\n",
            "                                                                                  'end_learning_rate': 0.0,\n",
            "                                                                                  'initial_learning_rate': 2e-05,\n",
            "                                                                                  'name': 'PolynomialDecay',\n",
            "                                                                                  'offset': 0,\n",
            "                                                                                  'power': 1.0},\n",
            "                                                                'type': 'polynomial'},\n",
            "                                           'optimizer': {   'lamb': {   'beta_1': 0.9,\n",
            "                                                                        'beta_2': 0.999,\n",
            "                                                                        'clipnorm': None,\n",
            "                                                                        'clipvalue': None,\n",
            "                                                                        'epsilon': 1e-06,\n",
            "                                                                        'exclude_from_layer_adaptation': None,\n",
            "                                                                        'exclude_from_weight_decay': [   'LayerNorm',\n",
            "                                                                                                         'layer_norm',\n",
            "                                                                                                         'bias'],\n",
            "                                                                        'global_clipnorm': None,\n",
            "                                                                        'name': 'LAMB',\n",
            "                                                                        'weight_decay_rate': 0.01},\n",
            "                                                            'type': 'lamb'},\n",
            "                                           'warmup': {   'linear': {   'name': 'linear',\n",
            "                                                                       'warmup_learning_rate': 0.0,\n",
            "                                                                       'warmup_steps': 200},\n",
            "                                                         'type': 'linear'}},\n",
            "                   'preemption_on_demand_checkpoint': True,\n",
            "                   'recovery_begin_steps': 0,\n",
            "                   'recovery_max_trials': 0,\n",
            "                   'steps_per_loop': 458,\n",
            "                   'summary_interval': 458,\n",
            "                   'train_steps': 2290,\n",
            "                   'train_tf_function': True,\n",
            "                   'train_tf_while_loop': True,\n",
            "                   'validation_interval': 458,\n",
            "                   'validation_steps': 51,\n",
            "                   'validation_summary_subdir': 'validation'}}\n"
          ]
        },
        {
          "data": {
            "application/javascript": [
              "google.colab.output.setIframeHeight(\"500px\");"
            ],
            "text/plain": [
              "\u003cIPython.core.display.Javascript object\u003e"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pp.pprint(exp_config.as_dict())\n",
        "display.Javascript('google.colab.output.setIframeHeight(\"500px\");')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aivSkEcMQI6c"
      },
      "source": [
        "## Create the `Task` object (`tfm.core.base_task.Task`) from the `config_definitions.TaskConfig`.\n",
        "\n",
        "The `Task` object has all the methods necessary for building the dataset, building the model, and running training \u0026 evaluation. These methods are driven by `tfm.core.train_lib.run_experiment`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ybBYjDU72754"
      },
      "outputs": [],
      "source": [
        "model_dir = './trained_model/'\n",
        "\n",
        "with distribution_strategy.scope():\n",
        "  task = task_factory.get_task(exp_config.task, logging_dir=model_dir)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uQvshzY-QoSi"
      },
      "source": [
        "## Train and Evaluate the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "executionInfo": {
          "elapsed": 2882433,
          "status": "ok",
          "timestamp": 1689013083414,
          "user": {
            "displayName": "Siva Sravana Kumar Neeli",
            "userId": "06669604936988620923"
          },
          "user_tz": 420
        },
        "id": "6zS8rSCqgHBz",
        "outputId": "05e314d0-fcca-4d3c-9d11-5822444f7b02"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "restoring or initializing model...\n",
            "train | step:      0 | training until step 458...\n",
            "train | step:    458 | steps/sec:    0.8 | output: \n",
            "    {'auc': 0.72914344,\n",
            "     'cls_accuracy': 0.66348255,\n",
            "     'learning_rate': 1.6e-05,\n",
            "     'training_loss': 0.63586426}\n",
            "saved checkpoint to ./trained_model/ckpt-458.\n",
            " eval | step:    458 | running 51 steps of evaluation...\n",
            " eval | step:    458 | steps/sec:    2.2 | eval time:   23.1 sec | output: \n",
            "    {'auc': 0.7645757,\n",
            "     'cls_accuracy': 0.67401963,\n",
            "     'steps_per_second': 2.2112783063481327,\n",
            "     'validation_loss': 0.6187927}\n",
            "train | step:    458 | training until step 916...\n",
            "train | step:    916 | steps/sec:    0.8 | output: \n",
            "    {'auc': 0.8580948,\n",
            "     'cls_accuracy': 0.7363537,\n",
            "     'learning_rate': 1.2e-05,\n",
            "     'training_loss': 0.5320095}\n",
            "saved checkpoint to ./trained_model/ckpt-916.\n",
            " eval | step:    916 | running 51 steps of evaluation...\n",
            " eval | step:    916 | steps/sec:    2.9 | eval time:   17.8 sec | output: \n",
            "    {'auc': 0.800959,\n",
            "     'cls_accuracy': 0.6960784,\n",
            "     'steps_per_second': 2.861756377583562,\n",
            "     'validation_loss': 0.5854072}\n",
            "train | step:    916 | training until step 1374...\n",
            "train | step:   1374 | steps/sec:    0.8 | output: \n",
            "    {'auc': 0.95597136,\n",
            "     'cls_accuracy': 0.83406115,\n",
            "     'learning_rate': 7.999999e-06,\n",
            "     'training_loss': 0.38442203}\n",
            "saved checkpoint to ./trained_model/ckpt-1374.\n",
            " eval | step:   1374 | running 51 steps of evaluation...\n",
            " eval | step:   1374 | steps/sec:    2.9 | eval time:   17.8 sec | output: \n",
            "    {'auc': 0.7954676,\n",
            "     'cls_accuracy': 0.6495098,\n",
            "     'steps_per_second': 2.861158673104126,\n",
            "     'validation_loss': 0.6343756}\n",
            "train | step:   1374 | training until step 1832...\n",
            "train | step:   1832 | steps/sec:    0.8 | output: \n",
            "    {'auc': 0.9869786,\n",
            "     'cls_accuracy': 0.91784936,\n",
            "     'learning_rate': 4e-06,\n",
            "     'training_loss': 0.26613}\n",
            "saved checkpoint to ./trained_model/ckpt-1832.\n",
            " eval | step:   1832 | running 51 steps of evaluation...\n",
            " eval | step:   1832 | steps/sec:    2.8 | eval time:   18.1 sec | output: \n",
            "    {'auc': 0.7897566,\n",
            "     'cls_accuracy': 0.65686274,\n",
            "     'steps_per_second': 2.8241461717336493,\n",
            "     'validation_loss': 0.66516936}\n",
            "train | step:   1832 | training until step 2290...\n",
            "train | step:   2290 | steps/sec:    0.8 | output: \n",
            "    {'auc': 0.9962825,\n",
            "     'cls_accuracy': 0.9585153,\n",
            "     'learning_rate': 0.0,\n",
            "     'training_loss': 0.18938588}\n",
            "saved checkpoint to ./trained_model/ckpt-2290.\n",
            " eval | step:   2290 | running 51 steps of evaluation...\n",
            " eval | step:   2290 | steps/sec:    2.4 | eval time:   21.0 sec | output: \n",
            "    {'auc': 0.7945166,\n",
            "     'cls_accuracy': 0.6764706,\n",
            "     'steps_per_second': 2.4306418804782743,\n",
            "     'validation_loss': 0.6767317}\n"
          ]
        }
      ],
      "source": [
        "model, eval_logs = train_lib.run_experiment(\n",
        "    distribution_strategy=distribution_strategy,\n",
        "    task=task,\n",
        "    mode='train_and_eval',\n",
        "    params=exp_config,\n",
        "    model_dir=model_dir)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "weZwyXKYQsVA"
      },
      "source": [
        "## Testing the trained model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZVZ33VldQ4vL"
      },
      "source": [
        "### Helper functions for pre-processing test data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O-q-hYpQ6nQB"
      },
      "outputs": [],
      "source": [
        "tokenizer = FastWordpieceBertTokenizer(\n",
        "    vocab_file=vocab_file,\n",
        "    lower_case=exp_config.task.train_data.lower_case\n",
        ")\n",
        "\n",
        "packer = BertPackInputs(\n",
        "    seq_length=exp_config.task.train_data.seq_length,\n",
        "    special_tokens_dict=tokenizer.get_special_tokens_dict()\n",
        ")\n",
        "\n",
        "\n",
        "class BertInputProcessor(tf.keras.layers.Layer):\n",
        "  def __init__(self, tokenizer, packer):\n",
        "    super().__init__()\n",
        "    self.tokenizer = tokenizer\n",
        "    self.packer = packer\n",
        "\n",
        "  def call(self, inputs):\n",
        "    tok1 = self.tokenizer(inputs['sentence1'])\n",
        "    tok2 = self.tokenizer(inputs['sentence2'])\n",
        "\n",
        "    packed = self.packer([tok1, tok2])\n",
        "\n",
        "    if 'label' in inputs:\n",
        "      return packed, inputs['label']\n",
        "    else:\n",
        "      return packed"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xu4OqanvROFz"
      },
      "source": [
        "### Pre-process test data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TYsP7Soq7off"
      },
      "outputs": [],
      "source": [
        "bert_inputs_processor = BertInputProcessor(\n",
        "    tokenizer=tokenizer,\n",
        "    packer=packer\n",
        ")\n",
        "test_ds = ds['test'].batch(\n",
        "    1).map(bert_inputs_processor)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w_I68G51RSHO"
      },
      "source": [
        "### Get the predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "executionInfo": {
          "elapsed": 10328,
          "status": "ok",
          "timestamp": 1689013095845,
          "user": {
            "displayName": "Siva Sravana Kumar Neeli",
            "userId": "06669604936988620923"
          },
          "user_tz": 420
        },
        "id": "zDABiqAn29oi",
        "outputId": "2ef6b773-1302-4659-e6bf-45552e4fae7f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Sentence 1:[b'Shares in BA were down 1.5 percent at 168 pence by 1420 GMT , off a low of 164p , in a slightly stronger overall London market .']\n",
            "Sentence 2:[b'Shares in BA were down three percent at 165-1 / 4 pence by 0933 GMT , off a low of 164 pence , in a stronger market .']\n",
            "1/1 [==============================] - 4s 4s/step\n",
            "Prediction: 1\n",
            "Sentence 1:[b'The South Korean Agriculture and Forestry Ministry also said it would throw out or send back all Canadian beef currently in store .']\n",
            "Sentence 2:[b'The South Korean Agriculture and Forestry Ministry said it would scrap or return all Canadian beef in store .']\n",
            "1/1 [==============================] - 0s 103ms/step\n",
            "Prediction: 1\n",
            "Sentence 1:[b'\" New Yorkers didn \\'t embrace these units like they could have , \" said Matthew Daus , chairman of the commission .']\n",
            "Sentence 2:[b'\" New Yorkers didn \\'t embrace these units like they could have , \" Matthew W. Daus , the commission \\'s chairman , said yesterday .']\n",
            "1/1 [==============================] - 0s 95ms/step\n",
            "Prediction: 1\n",
            "Sentence 1:[b'\" I really liked him and I still do , \" Cohen Alon told the Herald yesterday .']\n",
            "Sentence 2:[b'And I really liked him , and I still do .']\n",
            "1/1 [==============================] - 0s 96ms/step\n",
            "Prediction: 0\n",
            "Sentence 1:[b'Tight media controls and the remote location of the northern village where the fighting broke out made it impossible to confirm what happened .']\n",
            "Sentence 2:[b'Tight media controls and the remote location of the clash made it impossible to confirm what happened .']\n",
            "1/1 [==============================] - 0s 94ms/step\n",
            "Prediction: 1\n",
            "Sentence 1:[b'She had been critically ill after May 7 surgery to replace a heart valve .']\n",
            "Sentence 2:[b'She had been critically ill since having surgery at Baptist Hospital on May 7 to replace a heart valve .']\n",
            "1/1 [==============================] - 0s 97ms/step\n",
            "Prediction: 1\n",
            "Sentence 1:[b'\" In fact , I was physically sick several times at this stage , because he looked so desperate , \" she said .']\n",
            "Sentence 2:[b'Speaking about the day before he was found dead , she said : \" I was physically sick several times at this stage because he looked so desperate .']\n",
            "1/1 [==============================] - 0s 101ms/step\n",
            "Prediction: 0\n",
            "Sentence 1:[b'The technology-laced Nasdaq Composite Index added 11.98 points , or 0.72 percent , to 1,680.42 .']\n",
            "Sentence 2:[b\"The broader Standard \u0026 Poor 's 500 Index .SPX was off 1.07 points , or 0.11 percent , at 1,010.59 .\"]\n",
            "1/1 [==============================] - 0s 99ms/step\n",
            "Prediction: 0\n"
          ]
        }
      ],
      "source": [
        "for record in ds['test'].batch(1).take(8):\n",
        "  print(f\"Sentence 1:{record['sentence1'].numpy()}\")\n",
        "  print(f\"Sentence 2:{record['sentence2'].numpy()}\")\n",
        "  processed_rec = bert_inputs_processor(record)\n",
        "  prediction = tf.argmax(\n",
        "      model.predict(processed_rec[0]),\n",
        "      axis=1)\n",
        "  print(f\"Prediction: {prediction[0]}\")"
      ]
    }
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